Contents

Connectivity Visualizations

Connectivity plots

The connectivity plots provide a way to visualize pair-wise correlation, coherence, and connectivity measures between nodes. There are 2 types of plots, shown in the image below. Both types of plots can be generated as an animation to view changes over time or as standalone plots.

Connectivity Plot

Connectivity Circle

Lines drawn on a 2D representation of a skull

Lines drawn between nodes represented in a circle

General Setup

Import required modules

from simpl_eeg import connectivity, eeg_objects

import warnings
warnings.filterwarnings('ignore')

Note

Please include the line below in your IDE so that the changes would be simultaneously reflected when you make a change to the python scripts.**

%load_ext autoreload

%autoreload 2

Create epoched data

For additional options see Creating EEG Objects section.

experiment_folder = "../../data/927"
nth_epoch = 0

epochs = eeg_objects.Epochs(experiment_folder)
epoch = epochs.get_nth_epoch(nth_epoch)

Reading /Users/sasha/mds/simpl_eeg_capstone/data/927/fixica.fdt

Not setting metadata

Not setting metadata

33 matching events found

Setting baseline interval to [-0.2998046875, 0.0] sec

Applying baseline correction (mode: mean)

0 projection items activated

Loading data for 33 events and 2049 original time points ...

0 bad epochs dropped

Create a Connectivity Plot animation

Define parameters

A detailed description of all parameters can be found in the connectivity.animate_connectivity docstring:

help(connectivity.animate_connectivity)

Help on function animate_connectivity in module simpl_eeg.connectivity:

animate_connectivity(epoch, calc_type='correlation', steps=20, pair_list=[], threshold=0, show_sphere=True, colormap='RdBu_r', vmin=None, vmax=None, line_width=None, title=None, **kwargs)
    Animate 2d EEG nodes on scalp with lines representing connectivity
    
    Args:
        epochs (mne.epochs.Epochs): Epoch to visualize
        calc_type (str: Connectivity calculation type. Defaults to "correlation".
        pair_list ([str], optional): List of node pairs. Defaults to [], which indicates all pairs.
        threshold (int, optional): Unsigned connectivity threshold to display connection. Defaults to 0.
        show_sphere (bool, optional): Whether to show the cartoon head or not. Defaults to True.
        steps (int, optional): Number of frames to use in correlation caluclation. Defaults to 20.
        colormap (str, optional): Colour scheme to use. Defaults to "RdBu_r"
        vmin (int, optional): The minimum for the scale. Defaults to None.
        vmin (int, optional): The maximum for the scale. Defaults to None.
        line_width (int, optional): The line width for the connections. Defaults to None for non-static width.
        title (str, optional): The title to display on the plot. Defaults to None for no title.
        **kwargs (dict, optional): Optional arguments to pass to mne.viz.plot_sensors().
    
    Returns:
        matplotlib.animation.Animation: Animation of connectivity plot

vmin = -1
vmax = 1
colormap = "RdBu_r"
calc_type = "correlation"
pair_list = []  # select from the PAIR_OPTIONS below or use a custom pair.
line_width = None
steps = 50
threshold = 0
show_sphere = True

PAIR_OPTIONS = {
    "all_pairs": [],
    "local_anterior": "Fp1-F7, Fp2-F8, F7-C3, F4-C4, C4-F8, F3-C3",
    "local_posterior": "T5-C3, T5-O1, C3-P3, C4-P4, C4-T6, T6-O2",
    "far_coherence": "Fp1-T5, Fp2-T6, F7-T5, F7-P3, F7-O1, T5-F3, F3-P3, F4-P4, P4-F8, F8-T6, F8-O2, F4-T6",
    "prefrontal_to_frontal_and_central": "Fp1-F3, Fp1-C3, Fp2-F4, Fp2-C4",
    "occipital_to_parietal_and_central": "C3-O1, P3-O1, C4-O2, P4-O4",
    "prefrontal_to_parietal": "Fp1-P3, Fp2-P4",
    "frontal_to_occipital": "F3-O1, P4-O2",
    "prefrontal_to_occipital": "Fp1-O1, Fp2-O2"
}

# example of referencing a pair from the list
PAIR_OPTIONS["far_coherence"]

'Fp1-T5, Fp2-T6, F7-T5, F7-P3, F7-O1, T5-F3, F3-P3, F4-P4, P4-F8, F8-T6, F8-O2, F4-T6'

Generating the animation

%matplotlib inline

%%capture

anim = connectivity.animate_connectivity(
    epoch,
    calc_type=calc_type,
    steps=steps,
    pair_list=pair_list,
    threshold=threshold,
    show_sphere=show_sphere,
    colormap=colormap,
    vmin=vmin,
    vmax=vmax,
    line_width=line_width,
)

from IPython.display import HTML

html_plot = anim.to_jshtml()
video = HTML(html_plot)

video

Saving the animation

Save as html

html_file_path = "examples/connectivity.html"

html_file = open(html_file_path, "w")
html_file.write(html_plot)
html_file.close()

Save as gif

gif_file_path = "examples/connectivity.gif"

# set frames per second (fps) and resolution (dpi)
anim.save(gif_file_path, fps=3, dpi=300)

Save as mp4

mp4_file_path = "examples/connectivity.mp4"
anim.save(mp4_file_path, fps=3, dpi=300)

Note

If FFMpegWriter does not work on your computer you can save the file as a gif first and then convert it into mp4 file by running the code below.

import moviepy.editor as mp

clip = mp.VideoFileClip(gif_file_path)
clip.write_videofile(mp4_file_path)

Create a Connectivity Circle animation

Define parameters

A detailed description of all parameters can be found in the connectivity.animate_connectivity_circle docstring:

help(connectivity.animate_connectivity_circle)

Help on function animate_connectivity_circle in module simpl_eeg.connectivity:

animate_connectivity_circle(epoch, calc_type='correlation', max_connections=50, steps=20, colormap='RdBu_r', vmin=None, vmax=None, line_width=None, title=None, **kwargs)
    Animate connectivity circle
    
    Args:
        epoch (mne.epochs.Epochs): Epoch to visualize
        calc_type (str, optional): Connectivity calculation type. Defaults to "correlation".
        max_connections (int, optional): Number of connections to display. Defaults to 50.
        steps (int, optional): Number of frames to use in correlation caluclation. Defaults to 20.
        colormap (str, optional): Colour scheme to use. Defaults to "RdBu_r".
        vmin (int, optional): The minimum for the scale. Defaults to None.
        vmin (int, optional): The maximum for the scale. Defaults to None.
        line_width (int, optional): The line width for the connections. Defaults to None for non-static width.
        title (str, optional): The title to display on the plot. Defaults to None for no title.
        **kwargs (dict, optional): Optional arguments to pass to mne.viz.plot_connectivity_circle().
    
    Returns:
        matplotlib.animation.Animation: Animation of connectivity plot

vmin = -1
vmax = 1
colormap = "RdBu_r"
calc_type = "correlation"
line_width = 1
steps = 50
max_connections = 50

Generating the animation

%matplotlib notebook

%%capture

anim = connectivity.animate_connectivity_circle(
    epoch,
    calc_type=calc_type,
    max_connections=max_connections,
    steps=steps,
    colormap=colormap,
    vmin=vmin,
    vmax=vmax,
    line_width=line_width,
)

from IPython.display import HTML

html_plot = anim.to_jshtml()
video = HTML(html_plot)

video

Saving the animation

Save as html

html_file_path = "examples/connectivity_circle.html"
html_file = open(html_file_path, "w")
html_file.write(html_plot)
html_file.close()

Save as gif

gif_file_path = "examples/connectivity_circle.gif"

# set frames per second (fps) and resolution (dpi)
anim.save(gif_file_path, fps=3, dpi=300)

Save as mp4

mp4_file_path = "examples/connectivity_cicle.mp4"
anim.save(mp4_file_path, fps=3, dpi=300)

Note

If FFMpegWriter does not work on your computer you can save the file as a gif first and then convert it into mp4 file by running the code below.

import moviepy.editor as mp

clip = mp.VideoFileClip(gif_file_path)
clip.write_videofile(mp4_file_path)
3D Head Visualizations User Interface